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Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.

Quantum walks have been used to develop quantum algorithms since their inception, and can be seen as an alternative to the usual circuit model; combining single-particle quantum walks on sparse graphs with two-particle scattering on a line lattice is sufficient to perform universal quantum computation. In this work we solve the problem of two- particle scattering on the line lattice for a family of interactions without translation invariance, recovering the Bose-Hubbard interaction as the limiting case. Due to its generality, our systematic approach lays the groundwork to solve the more general problem of multi-particle scattering on general graphs, which in turn can enable design of different or simpler quantum gates and gadgets. As a consequence of this work, we show that a CPHASE gate can be achieved with high fidelity when the interaction acts only on a small portion of the line graph.

We present a variational quantum eigensolver (VQE) algorithm for the efficient bootstrapping of the causal representation of multiloop Feynman diagrams in the Loop-Tree Duality (LTD) or, equivalently, the selection of acyclic configurations in directed graphs. A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic configurations. The algorithm has been adapted to select multiple degenerated minima and thus achieves higher detection rates. A performance comparison with a Grover's based algorithm is discussed in detail. The VQE approach requires, in general, fewer qubits and shorter circuits for its implementation, albeit with lesser success rates.

We present an overview of the analysis of the multiloop topologies that appear for the first time at four loops and the assembly of them in a general expression, the N$^4$MLT universal topology. Based on the fact that the Loop-Tree Duality enables to open any scattering amplitude in terms of convolutions of known subtopologies, we go through the dual representation of the universal N$^4$MLT topology and the manifestly causal representation. Additionally, we expose the application of a quantum algorithm as an alternative methodology to identify the causal singular configurations of multiloop Feynman diagrams.

Initializing classical data in a quantum device is an essential step in many quantum algorithms. As a consequence of measurement and noisy operations, some algorithms need to reinitialize the prepared state several times during its execution. In this work, we propose a quantum state preparation algorithm called CVO-QRAM with computational cost O(kM), where M is the number of nonzero probability amplitudes and $k$ is the maximum number of bits with value 1 in the patterns to be stored. The proposed algorithm can be an alternative to create sparse states in future NISQ devices.

We investigate the formation of non-ground-state Bose-Einstein condensates within the mean-field description represented by the Gross-Pitaevskii equation (GPE). The objective is to form excited states of a condensate known as nonlinear topological modes, which are stationary solutions of the GPE. Nonlinear modes can be generated by modulating either the trapping potential or the atomic scattering length. We show that it is possible to coherently control the transitions to excited nonlinear modes by manipulating the relative phase of the modulations. In addition, we show that the use of both modulations can modify the speed of the transitions. In our analysis, we employ approximate analytical techniques, including a perturbative treatment, and numerical calculations for the GPE. Our study evidences that the coherent control of the GPE presents novel possibilities which are not accessible for the Schrödinger equation.

The impact of superconducting correlations on localized electronic states is important for a wide range of experiments in fundamental and applied superconductivity (SC). This includes scanning tunneling microscopy of atomic impurities at the surface of superconductors, as well as superconducting-ion-chip spectroscopy of neutral ions and Rydberg states. Moreover, atom-like centers close to the surface are currently believed to be the main source of noise and decoherence in qubits based on superconducting devices. The proximity effect is known to dress atomic orbitals in Cooper-pair-like states known as Yu-Shiba-Rusinov states (YSR), but the impact of SC on the measured orbital splittings and optical/noise transitions is not known. Here we study the interplay between orbital degenerescence and particle number admixture in atomic states, beyond the usual classical spin approximation. We model the atom as a generalized Anderson model interacting with a conventional $s$-wave superconductor. In the limit of zero on-site Coulomb repulsion ($U=0$), we obtain YSR subgap energy levels that are identical to the ones obtained from the classical spin model. When $\Delta$ is large and $U>0$, the YSR spectra is no longer quasiparticle-like, and the highly degenerate orbital subspaces are split according to their spin, orbital, and number-parity symmetry. We show that $U>0$ activates additional poles in the atomic Green's function, suggesting an alternative explanation for the peak splittings recently observed in scanning tunneling microscopy of orbitally-degenerate impurities in superconductors. We describe optical excitation and absorption of photons by YSR states, showing that many additional optical channels open up in comparison to the non-superconducting case. Conversely, the additional dissipation channels imply increased electromagnetic noise due to impurities in superconducting devices.

The experimental techniques have evolved to a stage where various examples of nanostructures with non-trivial shapes have been synthesized, turning the dynamics of a constrained particle and the link with geometry into a realistic and important topic of research. Some decades ago, a formalism to deduce a meaningful Hamiltonian for the confinement was devised, showing that a geometry-induced potential (GIP) acts upon the dynamics. In this work we study the problem of prescribed GIP for curves and surfaces in Euclidean space $\mathbb{R}^3$, i.e., how to find a curved region with a potential given \it a priori. The problem for curves is easily solved by integrating Frenet equations, while the problem for surfaces involves a non-linear 2nd order partial differential equation (PDE). Here, we explore the GIP for surfaces invariant by a 1-parameter group of isometries of $\mathbb{R}^3$, which turns the PDE into an ordinary differential equation (ODE) and leads to cylindrical, revolution, and helicoidal surfaces. Helicoidal surfaces are particularly important, since they are natural candidates to establish a link between chirality and the GIP. Finally, for the family of helicoidal minimal surfaces, we prove the existence of geometry-induced bound and localized states and the possibility of controlling the change in the distribution of the probability density when the surface is subjected to an extra charge.

In this paper we study the nondegenerate optical parametric oscillator with injected signal, both analytically and numerically. We develop a perturbation approach which allows us to find approximate analytical solutions, starting from the full equations of motion in the positive P-representation. We demonstrate the regimes of validity of our approximations via comparison with the full stochastic results. We and that, with reasonably low levels of injected signal, the system allows for demonstrations of quantum entanglement and the Einstein-Podolsky-Rosen paradox. In contrast to the normal optical parametric oscillator operating below threshold, these features are demonstrated with relatively intense felds.

We present a protocol to generate and control quantum entanglement between the states of two subsystems (the system ${\cal S}$) by making measurements on a third subsystem (the monitor ${\cal M}$), interacting with ${\cal S}$. For the sake of comparison we consider first an ideal, or instantaneous projective measurement, as postulated by von Neumann. Then we compare it with the more realistic or generalized measurement procedure based on photocounting on ${\cal M}$. Further we consider that the interaction term (between ${\cal S}$ and ${\cal M}$) contains a quantum nondemolition variable of ${\cal S}$ and discuss the possibility and limitations for reconstructing the initial state of ${\cal S}$ from information acquired by photocounting on ${\cal M}$.